Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
Open Access
- 29 August 2022
- journal article
- research article
- Published by MDPI AG in Biomimetics
- Vol. 7 (3), 124
- https://doi.org/10.3390/biomimetics7030124
Abstract
The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.Keywords
This publication has 50 references indexed in Scilit:
- A Peak Price Tracking-Based Learning System for Portfolio SelectionIEEE Transactions on Neural Networks and Learning Systems, 2017
- An alternative computational method for finding the minimum-premium insurance portfolioAIP Conference Proceedings, 2016
- Risk-budgeting multi-portfolio optimization with portfolio and marginal risk constraintsAnnals of Operations Research, 2015
- Fairness and Efficiency in Multiportfolio OptimizationOperations Research, 2014
- Particle Swarm Optimization (PSO) for the constrained portfolio optimization problemExpert Systems with Applications, 2011
- Multiportfolio Optimization: A Natural Next StepPublished by Springer Science and Business Media LLC ,2010
- On the generalization of probabilistic transformation methodApplied Mathematics and Computation, 2007
- Computational methods in portfolio insuranceApplied Mathematics and Computation, 2007
- Pooling Trades in a Quantitative Investment ProcessThe Journal of Portfolio Management, 2006
- Computational study of a family of mixed-integer quadratic programming problemsMathematical Programming, 1996